18-465/665: Advanced Probability & Statistics for Engineers

Course Description

This course will help masters and undergraduate students to obtain the background necessary for excelling in courses and careers in machine learning, artificial intelligence, and related fields. We will cover basic concepts of probability prerequisite to understanding the material typically taught in a ML course. We will also cover slightly more advanced topics including Markov Chains, hypothesis testing, and maximum-likelihood estimation. The remaining part of the semester will be devoted to introducing machine learning concepts such as supervised/unsupervised learning, model identification, clustering, expectation maximization, etc. Students should be familiar with basic calculus, linear algebra.

  • Number of Units: 12

  • Pre-requisite: Basic Calculus

  • Course Area: Artificial Intelligence, Robotics and Control

  • Tentative Syllabus: Given here

Instructor and Administrative Staff

  • Instructor: Prof. Osman Yağan                        diamond Teaching Assistants: Mansi Sood and Yichen Ruan

  • Office Location: HH A302                              

  • Email Address: oyagan@ece.cmu.edu         

  • Office Hours: Mondays 2:30pm-4pm                                            

Class Schedule

  • Lecture: Mondays and Wednesdays 12:30 pm – 2:20 pm (EST)

  • Recitation: Fridays 12:30 pm – 2:20 pm (EST)

  • Location: WEH 5328 (PIT) and B23 118/221 (SV)

Recommended Books

  • Papoulis and S. U. Pillai, Probability, Random Variables, and Stochastic Processes, 4th Ed.

  • B. Hajek, Random Processes for Engineers, Cambridge university press, 2015

  • Louis L Scharf, Statistical Signal Processing, Detection, Estimation, and Time Series Analysis. 1991, 1st Ed.

  • Vincent Poor, An Introduction to Signal Detection and Estimation, Springer, 2nd Ed.

  • Larry Wasserman, All of statistics: a concise course in statistical inference, Springer.


Homeworks (7 sets) 40%
Test 1 20%
Test 2 20%
Test 3 20%